{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "inputfile = 'attrsConstruction.xlsx'\n",
    "\n",
    "data = pd.read_excel(inputfile)\n",
    "df = data.iloc[:len(data)-5]\n",
    "\n",
    "inputfile1 = 'pedictdata_C.xlsx'# 预测值\n",
    "result = pd.read_excel(inputfile1)\n",
    "inputfile2 = 'pedictdata_D.xlsx'# 预测值\n",
    "result1 = pd.read_excel(inputfile2)\n",
    "\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure',figsize=(9,9))\n",
    "import datetime\n",
    "import matplotlib.dates as mdates\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']= False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SYS_NAME</th>\n",
       "      <th>CWXT_DB:184:C:\\</th>\n",
       "      <th>CWXT_DB:184:D:\\</th>\n",
       "      <th>COLLECTTIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>34270787.33</td>\n",
       "      <td>80262592.65</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>34328899.02</td>\n",
       "      <td>83200151.65</td>\n",
       "      <td>2014-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>34327553.50</td>\n",
       "      <td>83208320.00</td>\n",
       "      <td>2014-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>34288672.21</td>\n",
       "      <td>83099271.65</td>\n",
       "      <td>2014-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>34190978.41</td>\n",
       "      <td>82765171.65</td>\n",
       "      <td>2014-10-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SYS_NAME  CWXT_DB:184:C:\\  CWXT_DB:184:D:\\ COLLECTTIME\n",
       "0   财务管理系统      34270787.33      80262592.65  2014-10-01\n",
       "1   财务管理系统      34328899.02      83200151.65  2014-10-02\n",
       "2   财务管理系统      34327553.50      83208320.00  2014-10-03\n",
       "3   财务管理系统      34288672.21      83099271.65  2014-10-04\n",
       "4   财务管理系统      34190978.41      82765171.65  2014-10-05"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mz3/+M2effTa33347K1asYObMmezZs4fhw4cDcPvtt3PmmWcSHBxMs2YV00NP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Nmyhba2NgDKyso4fPgwjY2NAJSUlODxeNjlNiXMnDmT+fPnU1lZCUBWVhZlZWVUVVXR6V64ysvL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\nY44lMMYYY4wZc/4/uch3q9Nz7joAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f2c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig  = plt.figure()\n",
    "\n",
    "fig.set(alpha=0.2)#设置图标透明度\n",
    "\n",
    "ax = fig.add_subplot(2,1,1)\n",
    "ax.set_title(u\"C盘空间时序预测图\")\n",
    "\n",
    "ax.set(xlabel=u'日期',ylabel=u'磁盘使用大小')\n",
    "# 图上时间间隔显示为10天\n",
    "ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=range(1,32), interval=7)) \n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d\"))\n",
    "plt.subplots_adjust(bottom=0.13,top=0.95)\n",
    "ax.plot(df['COLLECTTIME'],df['CWXT_DB:184:C:\\\\'],'ro--',)\n",
    "ax.plot(result['COLLECTTIME'],result[u'实际值'],'g+--',)\n",
    "ax.plot(result['COLLECTTIME'],result[u'预测值'],'b*-',)\n",
    "ax.grid(axis='y',linestyle='--')\n",
    "ax.legend()\n",
    "fig.autofmt_xdate() #自动根据标签长度进行旋转\n",
    "'''for label in ax.xaxis.get_ticklabels():   #此语句完成功能同上\n",
    "       label.set_rotation(45)\n",
    "'''\n",
    "\n",
    "ax1 = fig.add_subplot(2,1,2)\n",
    "ax1.set_title(u\"D盘空间时序预测图\")\n",
    "# ax.set_xlabel(u'日期')\n",
    "ax1.set(xlabel=u'日期',ylabel=u'磁盘使用大小')\n",
    "# 图上时间间隔显示为10天\n",
    "ax1.xaxis.set_major_locator(mdates.DayLocator(bymonthday=range(1,32), interval=7)) \n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y-%m-%d\"))\n",
    "plt.subplots_adjust(bottom=0.13,top=0.95)\n",
    "ax1.plot(df['COLLECTTIME'],df['CWXT_DB:184:D:\\\\'],'co--',)\n",
    "ax1.plot(result1['COLLECTTIME'],result1[u'实际值'],'m+--',)\n",
    "ax1.plot(result1['COLLECTTIME'],result1[u'预测值'],'y*-',)\n",
    "ax1.grid(axis='y',linestyle='--')\n",
    "ax1.legend()\n",
    "fig.autofmt_xdate() #自动根据标签长度进行旋转\n",
    "'''for label in ax.xaxis.get_ticklabels():   #此语句完成功能同上\n",
    "       label.set_rotation(45)\n",
    "'''\n",
    "plt.savefig('data_predict_pic.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th>实际值</th>\n",
       "      <th>预测值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-12</td>\n",
       "      <td>35704312.58</td>\n",
       "      <td>35779972.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-13</td>\n",
       "      <td>35704980.73</td>\n",
       "      <td>35836938.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-14</td>\n",
       "      <td>34570385.45</td>\n",
       "      <td>35889600.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-15</td>\n",
       "      <td>34673820.69</td>\n",
       "      <td>35935428.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-16</td>\n",
       "      <td>34793245.31</td>\n",
       "      <td>35981255.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  COLLECTTIME          实际值          预测值\n",
       "0  2014-11-12  35704312.58  35779972.17\n",
       "1  2014-11-13  35704980.73  35836938.19\n",
       "2  2014-11-14  34570385.45  35889600.65\n",
       "3  2014-11-15  34673820.69  35935428.18\n",
       "4  2014-11-16  34793245.31  35981255.70"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
